Multiqubit Quantum Convolutional Neural Networks for Efficient AI-Driven Healthcare Analytics

Quantum computing holds considerable promise for artificial intelligence (AI) in clinical decision support systems (CDSS), particularly in resource-constrained environments. This paper investigates a multiqubit quantum convolutional neural network (MQ-CNN) for medical diagnostics, leveraging parameterized quantum circuits to process low-resource healthcare datasets. We evaluate the framework on three binary classification tasks: breast cancer (Wisconsin dataset), diabetes (Pima Indians), and heart failure prediction, using angle-encoded clinical features. The MQ-CNN achieves test accuracies of 82.4%, 98.7%, and 97.3% respectively, matching classical CNNs while reducing trainable parameters significantly. Comparative analysis shows the quantum model converges 22% faster than hybrid quantum-classical counterparts under identical training conditions. Robustness evaluations confirm ≤3 % accuracy degradation when subjected to 15% synthetic label noise. These results highlight the architecture's suitability for resource-constrained environments, demonstrating that quantum-enhanced feature extraction can maintain diagnostic accuracy while significantly reducing computational overhead. This work provides empirical evidence for near-term quantum machine learning in practical healthcare applications.